Media and entertainment companies in the second half of 2026 continue to face pressure on advertising operations. US ad spend is forecast to grow 9.5% this year, with connected TV up 13.8% and linear TV expected to decline 1.7%, per IAB.
On paper, that looks like another year of dollars moving from linear to CTV. In practice, many teams still manage campaigns through disconnected systems. They need to sell, traffic, measure and bill across linear, CTV, streaming and digital at the same time. Those channels often run on different order management systems, measurement standards and trafficking workflows. Each manual handoff adds another chance for delay, error or lost revenue.
AI can reduce some of this strain, but not as a separate bet on linear or CTV. The better opportunity is the manual work underneath both.
Short-term ad demand should not set the infrastructure plan
Event-driven spending can create a quick lift. Live sports, tentpole programming, political campaigns and major cultural moments still bring scale and advertising demand. Nielsen found that ad-supported television accounted for 74.2% of overall TV viewing in the fourth quarter.
But strong demand in a given quarter does not change the market’s direction. McKinsey reported that daily viewing hours on linear TV declined at a 4% CAGR from 2022 to 2024, while streaming grew 13% and social video grew 14%. Infrastructure decisions need to account for both realities.
Many media companies still run separate linear and CTV tracks because digital was added later. AI should be applied where those tracks share the same operational pain, with clear goals before any tool is chosen.
Three principles for AI that earn a real return
Cognizant’s latest research indicates that AI exposure is already 30% higher than previously forecast for 2032. For media companies, that makes selection more important. They should avoid applying AI across every workflow and instead identify which processes should change first, what business result they expect and how progress will be measured.
- Start where manual work delays campaign movement. The right entry point is work that still requires teams to move files, review assets or reconcile campaign data by hand. For broadcasters handling high volumes of ad creative, AI can support localization and content review, helping speed asset turnaround and improve consistency across markets. In content moderation, it can route sensitive issues to the right teams. Narrow tasks with clear business outcomes are the right first move.
- Put AI into the handoffs between order, delivery and billing. AI should support the points where work slows down, not sit beside the process as a separate experiment. For one media advertising client, Cognizant saw automation cut proposal creation time by 97% and improve planning speed by 75%, while reducing manual work across post-campaign financial workflows to help shorten billing cycles and speed revenue recognition. As these foundations mature, AI agents can help coordinate related steps across ad operations with less step-by-step human instruction.
- Measure cycle time and revenue friction before scaling. Many organizations are being asked to do more with AI without measuring whether it is improving the business. Useful measures include cycle time, manual touches, exception rates, reconciliation effort, billing accuracy and speed to revenue. A baseline before deployment, followed by 30-, 60-and 90-day tracking, gives teams a clearer view of whether AI is improving performance or simply adding another layer of technology. Over time, those results can help media companies refine workflows competitors cannot easily copy.
Do not let the channel debate hide the operating problem
The second half of 2026 is testing media companies across upfront commitments, fall programming, sports calendars and year-end planning. Ad dollar placement will keep getting attention, but the more important question is whether the operating layer can support linear, CTV, streaming and digital without adding more manual work behind each channel.
Companies that treat each channel as a separate operating problem will keep recreating the same handoffs, reconciliation gaps and billing delays. The stronger move is to use AI where those channels share the same strain, starting with the workflows behind selling, delivery and billing.
Opinions expressed by SmartBrief contributors are their own.
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